For many years researchers in the field of artificial intelligence have attempted to produce processor-based software architectures that ‘think’ more like humans. Some examples of technology that embody so-called ‘artificial intelligence’ include planners and networks of problem solving agents. To date, however, the use of agents and planners has been limited in both applicability and in speed. This is because these systems do not operate in the same way that the human brain functions.
For example, U.S. Pat. No. 5,546,594 to Wazumi discloses a problem solver that utilizes a network of agents. These agents can cooperate to solve a particular problem. Each agent in the network of agents includes information associated with the remaining agents and required to select agents suitable for a cooperative problem solving. This problem solver requires that each of the agents include information relevant to the other agents in the network with which that agent may need to cooperate with. This is a cumbersome process when new agents are added to the network because the agents are required to essentially assimilate with one another. Additionally, this problem solver requires the agents to know which other agents in the network can solve aspects of problems a priori. Thus, as the networks of agents expands, the amount of storage space required to facilitate problem solving using this network also increases.
Another exemplary prior art problem solver is disclosed on U.S. Pat. No. 6,851,115 to Cheyer et al. This patent discloses a software-based architecture including a number of cooperative agents. Communication between the agents is fostered by a facilitator agent. According to some embodiments disclosed in this patent, interagent communication languages are used to enable client agents to make requests in the form of arbitrary complex goal expressions that may be solved using the facilitator agent. The facilitator agent is responsible for matching requests, from users and agents, with descriptions of the capabilities of other agents within the network. The use of this facilitator agent, however, can also become cumbersome as the number of agents increase.
Therefore the present inventors have recognized a deficiency in the prior art systems for distributed problem solving and/or planning in that as the networks become larger, they inherently become much more inefficient. Moreover, with increasing size of the networks, additional memory is typically needed and is associated with, for example, each individual agent or a select group of agents in the network. Therefore, what is needed is a network of interrelated agents that can operate without prior knowledge regarding the other agents in the network. Accordingly, the present invention is directed to solving at least one of these exemplary problems associated with the prior art by providing a modular network of interrelated software agents or “cobots.”